We test three ML models on 48K congressional bills to see how well they can predict policy areas from bill text. Results show logistic regression performs best, with a certified weighted-F1 of ~0.88 within-Congress (0.877) and ~0.87 out-of-Congress (0.871).
Only 2% of congressional bills become law. We analyze 15K bills from 2021-2023 to understand what drives legislative success and failure.
A computational social science project that built a 47,000+ bill dataset from Congress.gov (115th-117th Congresses), with a co-sponsorship legislative graph and TF-IDF baseline models for 33-class policy-area classification (up to ~0.89 weighted F1 on full text), now available on Hugging Face.